324 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" "St" Fellowship research jobs in Singapore
Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
machine learning and AI acceleration. Perform performance, power, and area (PPA) analysis of processor and accelerator designs. Publish research findings in top-tier conferences and journals and contribute
-
@ NUS (https://engbio.syncti.org ) specializes in Synthetic Biology in which we engineer microbes with useful capabilities for medical and industrial applications and we are part of SynCTI at NUS (https
-
management systems, proliferation of energy efficient solutions, creation of a “car-lite” society, digitalization of the energy system enabling a ubiquitous smart grid architecture and establishing low carbon
-
, 102: 14623-14688, 2005. (https://doi.org/10.1073/pnas.0503524102) 2. N.Y. Fu et al., Inhibition of ubiquitin-mediated degradation of MOAP-1 by apoptotic stimuli promotes Bax function in mitochondria
-
independently and as part of a team Experience with machine learning and AI applications in engineering is advantageous We regret to inform that only shortlisted candidates will be notified. Hiring Institution
-
accelerator design, verification, and physical implementation using open-source tools. Explore architecture-algorithm co-design for machine learning and AI acceleration. Perform performance, power, and area
-
multicultural hub and a leading Asian center for quantum technologies. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/IRL3654-CORHUN-036/Default.aspx Requirements Research FieldPhysicsEducation LevelPhD
-
, Bioinformatics, Computational Biology, or other AI-related disciplines. Strong foundation in AI, statistical modeling, machine learning, or high-dimensional data analysis. Proficiency in programming languages
-
Materials, Bioinspired Materials and Sustainable Materials. For more details, please view https://www.ntu.edu.sg/mse/research . We are seeking a highly motivated and interdisciplinary research fellow to
-
of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop software prototypes for AI-for-Science systems tailored to scientific discovery and data